Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal.

Abstract

Background: COVID-19 has been a major global health concern, severely impacting Nepal with thousands of cases and deaths. The patterns of COVID-19 incidence in the country may have varied over time during the pandemic, with geographic factors playing different roles in the early, middle, and later phases of transmission.

Methods: We utilized spatial statistics and GeoDetector methods to analyze district-level variations in COVID-19 incidence across Nepal from January 2020 to December 2022 using laboratory confirmed cases of the disease and a range of physical, biological and socioenvironmental explanatory variables. The analysis focused on identifying spatial patterns, hotspots, and key driving factors contributing to the uneven distribution of COVID-19 cases.

Results: We found an uneven distribution of COVID-19 in Nepal, with persistent hotspots in major cities, such as Kathmandu and Pokhara, reaching up to 133 cases per 1000 population. GeoDetector analysis identified the key drivers, including road density (q = 0.59, p < 0.001), ICU bed distribution (q = 0.51, p < 0.001), and population density (q = 0.46, p < 0.001). While natural environmental factors such as temperature, precipitation, and NDVI had low and statistically insignificant independent explanatory power, their interaction with variables such as nighttime light, NDVI, and population density enhanced explanatory strength, highlighting the complex spatial distribution of COVID-19 incidence.

Conclusions: We recommend that the Nepalese government implement more targeted and region-specific interventions to address COVID-19 outbreaks, especially in persistent hotspot areas, such as Kathmandu and other emerging cities.